Another common direction for redefining the style loss hearkens back to earlier patch-based methods. These methods explicitly match vectors of neural features (or 'patches' of such vectors) extracted from the style to vectors extracted from the content, creating concrete 'target features' for every spatial location in the output. Then the output image is optimized to produce this representation. Li et al. [1]} replaces the style loss of
[2]} with minimizing the distance between each patch of output features and their current nearest neighbor patch of style features.
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